Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs inversion models using different input features. The study obtained ground and unmanned aerial vehicle (UAV) hyperspectral images and chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) from sampling sites at three growth stages of glycyrrhiza (regreening, flowering, and maturity). Hyperspectral data were smoothed using the Savitzky–Golay filter, and the feature vegetation index was selected using the Pearson Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE). Feature extraction was performed using Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Successive Projections Algorithm (SPA). The SPAD values were then inverted using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and the results were analyzed visually. The results indicate that in the ground glycyrrhiza inversion model, the GA-XGBoost model combination performed best during the regreening period, with R², RMSE, and MAE values of 0.95, 0.967, and 0.825, respectively, showing improved model accuracy compared to full-spectrum methods. In the UAV glycyrrhiza inversion model, the CARS-PLSR combination algorithm yielded the best results during the maturity stage, with R², RMSE, and MAE values of 0.83, 1.279, and 1.215, respectively. This study proposes a method combining feature selection techniques and machine learning algorithms that can provide a reference for rapid, nondestructive inversion of glycyrrhiza SPAD at different growth stages using hyperspectral sensors. This is significant for monitoring the growth of glycyrrhiza, managing fertilization, and advancing precision agriculture.

Nitrogen plays a crucial role in cotton growth, making the precise diagnosis of its nutrition levels vital for the scientific and rational application of fertilizers. Addressing this need, our study introduced an EMRDFC-based diagnosis model specifically for cotton nitrogen nutrition levels. In our field experiments, cotton was subjected to five different nitrogen application rates. To enhance the diagnostic capabilities of our model, we employed ResNet101, MobileNetV2, and DenseNet121 as base models and integrated the CBAM (Convolutional Block Attention Module) into each to improve their ability to differentiate among various nitrogen levels. Additionally, the Focal loss function was introduced to address issues of data imbalance. The model’s effectiveness was further augmented by employing integration strategies such as relative majority voting, simple averaging, and weighted averaging. Our experimental results indicated significant accuracy improvements in the enhanced ResNet101, MobileNetV2, and DenseNet121 models by 2.3%, 2.91%, and 2.93%, respectively. Notably, the integration of these models consistently improved accuracy, with gains of 0.87% and 1.73% compared to the highest-performing single model, DenseNet121FC. The optimal ensemble model, which utilized the weighted average method, demonstrated superior learning and generalization capabilities. The proposed EMRDFC model shows great promise in precisely identifying cotton nitrogen status, offering critical insights into the diagnosis of crop nutrient status. This research contributes significantly to the field of agricultural technology by providing a reliable tool for nitrogen-level assessment in cotton cultivation.